A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction

Abstract

Accurate protein-ligand binding affinity prediction is crucial in drug discovery. Existing methods are predominately docking-free, without explicitly considering atom-level interaction between proteins and ligands in scenarios where crystallized protein-ligand binding conformations are unavailable. Now, with breakthroughs in deep learning AI-based protein folding and binding conformation prediction, can we improve binding affinity prediction? This study introduces a framework, Folding-Docking-Affinity (FDA), which folds proteins, determines protein-ligand binding conformations, and predicts binding affinities from three-dimensional protein-ligand binding structures. Our experimental results indicate that FDA performs comparably to state-of-the-art docking-free methods. We anticipate that our proposed framework serves as a starting point for integrating binding structures for more accurate binding affinity prediction. Accurate protein-ligand binding affinity prediction is crucial in drug discovery, however, most existing methods are docking-free without considering the binding conformations. Here, the authors report Folding-Docking-Affinity (FDA) framework that considers the computed 3D protein-ligand binding structures and which performs comparably to state-of-the-art docking-free methods.

Topics & Concepts